[1] We present the first observations of electron cyclotron harmonic waves at the Earth's bow shock from STEREO and Wind burst waveform captures. These waves are observed at magnetic field gradients at a variety of shock geometries ranging from quasi-parallel to nearly perpendicular along with whistler mode waves, ion acoustic waves, and electrostatic solitary waves. Large amplitude cyclotron harmonic waveforms are also observed in the magnetosheath in association with magnetic field gradients convected past the bow shock. Amplitudes of the cyclotron harmonic waves range from a few tens to more than 500 mV/m peak-peak. A comparison between the short (15 m) and long (100 m) Wind spin plane antennas shows a similar response at low harmonics and a stronger response on the short antenna at higher harmonics. This indicates that wavelengths are not significantly larger than 100 m, consistent with the electron cyclotron radius. Waveforms are broadband and polarizations are distinctively comma-shaped with significant power both perpendicular and parallel to the magnetic field. Harmonics tend to be more prominent in the perpendicular directions. These observations indicate that the waves consist of a combination of perpendicular Bernstein waves and field-aligned waves without harmonics. A likely source is the electron cyclotron drift instability which is a coupling between Bernstein and ion acoustic waves. These waves are the most common type of high-frequency wave seen by STEREO during bow shock crossings and magnetosheath traversals and our observations suggest that they are an important component of the high-frequency turbulent spectrum in these regions.

The requirement that planetary systems be dynamically stable is often used to vet new discoveries or set limits on unconstrained masses or orbital elements. This is typically carried out via computationally expensive N-body simulations. We show that characterizing the complicated and multidimensional stability boundary of tightly packed systems is amenable to machine learning methods. We find that training an XGBoost machine learning algorithm on physically motivated features yields an accurate classifier of stability in packed systems. On the stability timescale investigated (10 7 orbits), it is 3 orders of magnitude faster than direct N-body simulations. Optimized machine learning classifiers for dynamical stability may thus prove useful across the discipline, e.g., to characterize the exoplanet sample discovered by the upcoming Transiting Exoplanet Survey Satellite (TESS). This proof of concept motivates investing computational resources to train algorithms capable of predicting stability over longer timescales and over broader regions of phase space.

It has recently been proposed that Earth-like planets in the outer regions of the habitable zone experience unstable climates, repeatedly cycling between glaciated and deglaciated climatic states (Menou 2015). While this result has been confirmed and also extended to explain early Mars climate records Batalha et al. 2016), all existing work relies on highly idealized low-dimensional climate models. Here, we confirm that the phenomenology of climate cycles remains in 3D Earth climate models with considerably more degrees of freedom. To circumvent the computational barrier of integrating climate on Gyr timescales, we implement a hybrid 0D-3D integrator which uses a general circulation model (GCM) as a short relaxation step along a long evolutionary climate sequence. We find that GCM climate cycles are qualitatively consistent with reported lowdimensional results. This establishes on a firmer ground the notion that outer habitable zone planets may be preferentially found in transiently glaciated states.

Habitable planets are commonly imagined to be temperate planets like Earth, with areas of open ocean and warm land. In contrast, planets in snowball states, where oceans are entirely ice covered, are believed to be inhospitable. However, we show using a general circulation model that terrestrial planets in the inner habitable zone are able to support large unfrozen areas of land while in a snowball state. Due to their lower albedo, these unfrozen regions reach summer temperatures in excess of 10 °C. Such conditions permit CO2 weathering, suggesting that continental weathering can provide a mechanism for trapping planets in stable snowball states. The presence of land areas with warm temperatures and liquid surface water motivates a more‐nuanced understanding of habitability during these snowball events.

No abstract

Recently Nakamura and Huang proposed a semiempirical, one-dimensional model of atmospheric blocking based on the observed budget of local wave activity in the boreal winter. The model dynamics is akin to that of traffic flow, wherein blocking manifests as traffic jams when the streamwise flux of local wave activity reaches capacity. Stationary waves modulate the jet stream’s capacity to transmit transient waves and thereby localize block formation. Since the model is inexpensive to run numerically, it is suited for computing blocking statistics as a function of climate variables from large-ensemble, parameter sweep experiments. We explore sensitivity of blocking statistics to (i) stationary wave amplitude, (ii) background jet speed, and (iii) transient eddy forcing, using frequency, persistence, and prevalence as metrics. For each combination of parameters we perform 240 runs of 180-day simulations with aperiodic transient eddy forcing, each time randomizing the phase relations in forcing. The model climate shifts rapidly from a block-free state to a block-dominant state as the stationary wave amplitude is increased and/or the jet speed is decreased. When eddy forcing is increased, prevalence increases similarly but frequency decreases as blocks merge and become more persistent. It is argued that the present-day climate lies close to the boundary of the two states and hence its blocking statistics are sensitive to climate perturbations. The result underscores the low confidence in GCM-based assessment of the future trend of blocking under a changing climate, while it also provides a theoretical basis for evaluating model biases and understanding trends in reanalysis data.

The discovery of a large number of terrestrial exoplanets in the habitable zones of their stars, many of which are qualitatively different from Earth, has led to a growing need for fast and flexible 3D climate models, which could model such planets and explore multiple possible climate states and surface conditions. We respond to that need by creating ExoPlaSim, a modified version of the Planet Simulator (PlaSim) that is designed to be applicable to synchronously rotating terrestrial planets, planets orbiting stars with non-solar spectra, and planets with non-Earth-like surface pressures. In this paper we describe our modifications, present validation tests of ExoPlaSim’s performance against other GCMs, and demonstrate its utility by performing two simple experiments involving hundreds of models. We find that ExoPlaSim agrees qualitatively with more-sophisticated GCMs such as ExoCAM, LMDG, and ROCKE-3D, falling within the ensemble distribution on multiple measures. The model is fast enough that it enables large parameter surveys with hundreds to thousands of models, potentially enabling the efficient use of a 3D climate model in retrievals of future exoplanet observations. We describe our efforts to make ExoPlaSim accessible to non-modellers, including observers, non-computational theorists, students, and educators through a new Python API and streamlined installation through pip, along with online documentation.

Radio interferometers consisting of identical antennas arranged on a regular lattice permit fast Fourier transform beamforming, which reduces the correlation cost from O(n 2 ) in the number of antennas to O(n log n). We develop a formalism for describing this process and apply this formalism to derive a number of algorithms with a range of observational applications. These include algorithms for forming arbitrarily pointed tied-array beams from the regularly spaced Fourier-transform formed beams, sculpting the beams to suppress sidelobes while only losing percentlevel sensitivity, and optimally estimating the position of a detected source from its observed brightness in the set of beams. We also discuss the effect that correlations in the visibility-space noise, due to cross-talk and sky contributions, have on the optimality of Fourier transform beamforming, showing that it does not strictly preserve the sky information of the n 2 correlation, even for an idealized array. Our results have applications to a number of upcoming interferometers, in particular the Canadian Hydrogen Intensity Mapping Experiment-Fast Radio Burst (CHIME/FRB) project.